A multidimensional Mendelian randomization study on the impact of gut dysbiosis on chronic diseases and human longevity
Bibliographic record
Abstract
Abstract Alterations of the gut microbiota, often referred to as gut dysbiosis, have been associated with several chronic diseases and longevity in pre-clinical models as well as in observational studies. Whether these relationships underlie causal associations in humans remains to be established. We aimed to determine whether gut dysbiosis influences the risk of chronic diseases and longevity using a comprehensive 2-Sample Mendelian randomization (2SMR) approach. We included as exposures inflammatory bowel disease (IBD) as a human model of gut dysbiosis, 11 gut-associated metabolites and pathways and 48 microbial taxa. Study outcomes included eight chronic diseases previously linked with gut dysbiosis using observational studies (Alzheimer’s disease, depression, type 2 diabetes, non-alcoholic fatty liver disease, coronary artery disease (CAD), stroke, osteoporosis and chronic kidney disease) as well as parental longevity and life expectancy. Neither IBD, nor gut-associated metabolites were causally associated with chronic disease or lifespan. After multiple testing correction for 582 tests, no microbial taxa-chronic disease associations remained significant. After robustness analyses and multivariate MR to correct for body mass index and alcohol intake on all 42 nominally significant causal relationships, four associations remained. Altogether, results of this multidimensional Mendelian randomization study suggest that gut dysbiosis has little impact on chronic diseases and human longevity and that previous documented associations may not underly causal relationships. Studies with larger sample sizes and more optimal taxonomic discrimination may ultimately be required to determine whether the human gut microbiota plays a causal role in the etiology of chronic diseases and longevity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".